Systems and methods for automatic camera installation guide (cig)

ABSTRACT

Three-dimensional (3D) depth imaging systems and methods are disclosed for assessing an orientation with respect to a container. A 3D-depth camera captures 3D image data of a shipping container. A container feature assessment application determines a representative container point cloud and (a) converts the 3D image data into 2D depth image data; (b) compares the 2D depth image data to one or more template image data; (c) performs segmentation to extract 3D point cloud features; (d) determines exterior features of the shipping container and assesses the exterior features using an exterior features metric; (e) determines interior features of the shipping container and assesses the interior features using an interior features metric; and (f) generates an orientation adjustment instruction for indicating to an operator to orient the 3D-depth camera in a second direction for use during a shipping container loading session, wherein the second direction is different than the first direction.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.16/670,446, filed on Oct. 31, 2019, now U.S. Pat. No. 10,820,307, whichis incorporated herein by reference in its entirety.

BACKGROUND OF THE INVENTION

In the transportation industry, shipping containers (e.g., shippingcontainers as used in air and/or ground transportation and shipping,such as unit load devices (ULDs)) are typically loaded using a varietyof different techniques that take into account a variety of differentsizes and configurations of boxes, packages, or other items for shippingor transit. In addition, shipping containers, themselves, typically havevarious sizes and storage capacities (e.g., where such shippingcontainers are constructed to handle different cargo sizes, loads and/orconfigurations). Correspondingly, a major point of emphasis in thetransportation/shipping industry is performing high fidelity analyticsconcerning the loading of such containers.

Traditional analytics systems feature a camera (e.g., a load monitoringunit (LMU)) positioned at a designated loading point. These cameras willcapture images of shipping containers placed at the designated loadingpoint to facilitate analytics of the loading procedures used to loadeach container. However, problems arise from such traditional analyticssystems.

For example, accurate camera (e.g., LMU) orientation is essential foranalytical algorithms, such as ULD fullness algorithms, to achieveacceptable performance. LMUs are traditionally oriented uponinstallation through manual analysis of previously captured images.Thus, the traditional orientation process is very time consuming andinaccurate due to the inherent inaccuracies associated with human visualimage inspection. Moreover, large-scale installations may involveorienting dozens of LMUs, which can quickly compound theseinefficiencies.

Several conventional techniques attempt to solve these problems. Each,however, has specific drawbacks. For example, a direct 3D matchingtechnique may be employed to match a target point cloud to a 3D templatepoint cloud. However, the direct 3D matching technique is not robust inthat it lacks stable and repeatable results, is also sensitive topartial structures, and involves in high computation complexity. Inaddition, the matching is not accurate, which generally leads toerroneous and generally inaccurate reporting.

Another conventional technique includes point cloud clustering. Pointcloud clustering, however, is also not robust as it lacks stable andrepeatable results, in particular, it suffers from uncontrollable 3Ddata segmentation results. The point cloud clustering technique isadditionally sensitive to “noise” (e.g., loaders/personnel movingthrough the loading area) and small object interference (e.g., a packagebeing moved within the loading area). Because of this, point cloudclustering typically creates incorrect clustering results due to loaderand package interference.

Accordingly, various problems generally arise regarding how todynamically configure camera orientation automatically, efficiently, andaccurately during installation. Thus, there is a need forthree-dimensional (3D) depth imaging systems and methods for dynamiccamera orientation configuration that allow for fast and efficientreal-time orientation assessments for camera installation.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

The patent or application file contains at least one drawing executed incolor. Copies of this patent or patent application publication withcolor drawing(s) will be provided by the Office upon request and paymentof the necessary fee.

The accompanying figures, where like reference numerals refer toidentical or functionally similar elements throughout the separateviews, together with the detailed description below, are incorporated inand form part of the specification, and serve to further illustrateembodiments of concepts that include the claimed invention, and explainvarious principles and advantages of those embodiments.

FIG. 1 is a perspective view, as seen from above, of a predefined searchspace of a loading facility that depicts a load monitoring unit (LMU)having a 3D camera oriented in a first direction to capture 3D imagedata of a shipping container located in a space, in accordance withexample embodiments herein.

FIG. 2 is a perspective view of the LMU of FIG. 1, in accordance withexample embodiments herein.

FIG. 3 is a block diagram representative of an embodiment of a serverassociated with the loading facility of FIG. 1 and the 3D camera of FIG.2.

FIG. 4 is a flow chart for a 3D depth imaging algorithm for assessing anorientation with respect to a container, in accordance with exampleembodiments herein.

FIGS. 5A-5B illustrate example embodiments of 3D and 2D images regardingpre-processing and converting a depth image in accordance with FIG. 4,and in accordance with example embodiments herein.

FIGS. 6A-6C illustrate example embodiments of 3D and 2D images regardingpoint cloud to depth image conversion in accordance with FIG. 4, and inaccordance with example embodiments herein.

FIG. 7 illustrates an example embodiment of 2D images regarding templateextraction and matching in accordance with FIG. 4, and in accordancewith example embodiments herein.

FIGS. 8A-8C illustrate example diagrams and embodiments of 3D imagesregarding container back-wall and ground plane segmentation and planeregression in accordance with FIG. 4, and in accordance with exampleembodiments herein.

FIGS. 9A-9B illustrate example embodiments of 3D and 2D images regardingfront plane segmentation in accordance with FIG. 4, and in accordancewith example embodiments herein.

FIG. 10 illustrates an example embodiment of a 2D image regarding afront panel completeness analysis in accordance with FIG. 4, and inaccordance with example embodiments herein.

FIGS. 11A-11B illustrate example diagrams and embodiments of 3D imagesregarding front panel and back wall height estimation and back wallocclusion computation in accordance with FIG. 4, and in accordance withexample embodiments herein.

FIGS. 12A-12B illustrate example embodiments of 3D images regarding ULDvisibility and occlusion status creation and ULD 3D bounding boxcreation in accordance with FIG. 4, and in accordance with exampleembodiments herein.

Skilled artisans will appreciate that elements in the figures areillustrated for simplicity and clarity and have not necessarily beendrawn to scale. For example, the dimensions of some of the elements inthe figures may be exaggerated relative to other elements to help toimprove understanding of embodiments of the present invention.

The apparatus and method components have been represented whereappropriate by conventional symbols in the drawings, showing only thosespecific details that are pertinent to understanding the embodiments ofthe present invention so as not to obscure the disclosure with detailsthat will be readily apparent to those of ordinary skill in the arthaving the benefit of the description herein.

DETAILED DESCRIPTION OF THE INVENTION

Accordingly, systems and methods are described herein that provideautomatic assessment of orientations with respect to containers for LMUinstallation. The systems and methods described herein replaceconventional manual LMU installation processes by automaticallyproviding highly accurate LMU orientation information to analyticalalgorithms in real-time. The present disclosure proposes an efficient,accurate, and robust approach to dynamically orient LMUs duringinstallation to improve the efficiency and accuracy of the LMUinstallation process when compared to known analytical algorithms.

In addition, the present disclosure describes inventive embodiments thateliminate adjustments to the LMU orientation based on visual assessmentsof captured images. In contrast to conventional systems and methods,which either provide unstable or otherwise uncontrollable results, theembodiments of the present disclosure produce consistent, accurateinstallation instructions. Without the benefits of the presentdisclosure, installation orientation efforts would remain substantialand tedious.

At a high level, the systems and methods of the present disclosureprovide feedback to an LMU installer, and guide the LMUlocation/orientation to a good position that facilitates containerloading analytics (CLA). The proposed method utilizes both a 3D pointcloud and a 2D depth image template matching algorithm. For templateselection, the point cloud of a complete ULD is selected as a templateextraction source and converted to a depth image. A penalized leastsquares method is then used to fill up the missing data in the depthimage. Afterwards, a specific area including a middle portion of the ULDcontained in the depth image is chosen and cropped as the template forthe corresponding matching process.

After a live image of the ULD is captured, the embodiments of thepresent disclosure include matching the pre-selected template with thelive image. To facilitate this matching, the target point cloud is firstconverted to a depth image and then matched to the template. The matchedarea is then used to locate the ground and back-wall position in thescene. Following template matching, the 3D point cloud is segmented intoa ground plane and a back wall based on the matching location. Thesegmented ground and back wall are fed to the plane regression pipelinewhere each plane parameter is estimated. The ULD front plane is thensegmented based on known ULD dimensions.

For example, the ULD front plane is divided to several edges including aleft, a right, and a top edge. By calculating the ratio of each edge,the completeness of the frontal structure can be quickly and robustlyidentified. Moreover, based on known assumptions, such as the left sidewall is vertical to ground and the front panel, the systems and methodsof the present disclosure may directly infer both the location and fitof the ULD left wall, right wall, and top ceiling planes. The occlusionratio of the back wall to the front plane is then computed, and all sixplanes of the ULD container are localized to identify the bounding boxof the container. Finally, the completeness of the ULD front panel andthe occlusion of the back wall are computed to provide installationfeedback to the installer.

The 3D depth imaging systems and methods disclosed herein may be furtherappreciated by the various Figures disclosed herein.

FIG. 1 is a perspective view, as seen from above, of a space 101 withina loading facility that depicts a load monitoring unit (LMU) having a 3Dcamera (e.g., a 3D-depth camera) oriented in a direction to capture 3Dimage data of a shipping container, in accordance with exampleembodiments herein. As depicted, shipping container 102 has a shippingcontainer type of “AMJ.” Generally, a shipping container is selectedfrom one of several differently dimensioned containers. In variousembodiments, shipping containers may comprise any type of unit loaddevice (ULD). For example, a shipping container type may be of any ULDtype, including, for example, any of an AMJ type, an AAD type, an AKEtype, an AYY type, a SAA type, and APE type, or an AQF type. For ULDshipping containers, the first letter (e.g., “A” for “Certified aircraftcontainer”) indicates a specific type of ULD container, such ascertified, thermal, etc., the second letter represents base size interms of dimensions (e.g., “M” for 96×125 inch), and the third letterrepresents a side contour size and shape (e.g., “J” for a cube shapedULD container having a diagonal sloping roof portion on one side only).More generally, however, a shipping container may be any aircraft-basedshipping container.

The space 101 may be a predefined search space determined based on theshipping container size, dimensions, or otherwise configuration and/orthe area in which the shipping area is localized. For example, in oneembodiment, the predefined search space may be determined based on ULDtype, shape, or position within a general area. As shown in FIG. 1, forexample, the predefined search space is determined based on the size anddimensions of the shipping container 102 which is of type AMJ. Ingeneral, space 101 is defined so as to completely (or at leastpartially) include or image the shipping container. The space 101 mayfurther include a frontal area 103 that generally defines a frontposition of the predefined search space and/or shipping container 102.

FIG. 1 additionally depicts, within space 101, personnel or loaders 105and 106 that load packages 104 and 107 into the shipping container 102.In the embodiment of FIG. 1, shipping container 102 is being loaded byloaders 105 with packages 104 and 107 during a loading session. Theloading session includes loading a set or group of identified packagesinto shipping container 102. The loaders 105 and 106 and packages 104and 107, by movement through the space 101, may generally causeocclusion and interference with the 3D camera 202 (as discussed for FIG.2) capturing 3D image data, over time, of shipping container 102. Thus,determining the correct orientation of the 3D camera 202 duringinstallation is critical to ensure that improper installation does notfurther complicate the imaging difficulties posed by occlusion andinterference during normal operations of a loading session.

FIG. 2 is a perspective view of the LMU of FIG. 1, in accordance withexample embodiments herein. In various embodiments, LMU 202 is amountable device. Generally, an LMU 202 comprises camera(s) and aprocessing board and is configured to capture data of a loading scene(e.g., a scene including space 101). LMU 202 may run container fullnessestimation and other advanced analytical algorithms.

LMU 202 may include a 3D camera 254 for capturing, sensing, or scanning3D image data/datasets. For example, in some embodiments, the 3D camera254 may include an Infra-Red (IR) projector and a related IR camera. Insuch embodiments, the IR projector projects a pattern of IR light orbeams onto an object or surface, which, in various embodiments herein,may include surfaces or areas of a predefined search space (e.g., space101) or objects within the predefined search area, such as boxes orpackages (e.g., packages 104 and 107) and storage container 102. The IRlight or beams may be distributed on the object or surface in a patternof dots or points by the IR projector, which may be sensed or scanned bythe IR camera. A depth-detection app, such as a depth-detection appexecuting on the one or more processors or memories of LMU 202, candetermine, based on the pattern of dots or points, various depth values,for example, depth values of predefined search space 101. For example, anear-depth object (e.g., nearby boxes, packages, etc.) may be determinedwhere the dots or points are dense, and distant-depth objects (e.g., farboxes, packages, etc.) may be determined where the points are morespread out. The various depth values may be used by the depth-detectionapp and/or LMU 202 to generate a depth map. The depth map may representa 3D image of, or contain 3D image data of, the objects or surfaces thatwere sensed or scanned by the 3D camera 254, for example, the space 101and any objects, areas, or surfaces therein.

LMU 202 may further include a photo-realistic camera 256 for capturing,sensing, or scanning 2D image data. The photo-realistic camera 256 maybe an RGB (red, green, blue) based camera for capturing 2D images havingRGB-based pixel data. In some embodiments, the photo-realistic camera256 may capture 2D images, and related 2D image data, at the same orsimilar point in time as the 3D camera 254 such that the LMU 202 canhave both sets of 3D image data and 2D image data available for aparticular surface, object, area, or scene at the same or similarinstance in time.

In various embodiments as described herein, LMU 202 may be a mountabledevice that includes a 3D camera for capturing 3D images (e.g., 3D imagedata/datasets) and a photo-realistic camera (e.g., 2D imagedata/datasets). The photo-realistic camera may be an RGB camera forcapturing 2D images, such as the image of FIG. 1. LMU 202 may alsoinclude one or more processors and one or more computer memories forstoring image data, and/or for executing apps that perform analytics orother functions as described herein. In various embodiments, and asshown in FIG. 1, the LMU 202 may be mounted within a loading facilityand oriented in the direction of space 101 to capture 3D and/or 2D imagedata of shipping container 102. For example, as shown in FIG. 1, LMU 202may be oriented such that the 3D and 2D cameras of LMU 202 may capture3D image data of shipping container 102, e.g., where LMU 202 may scan orsense the walls, floor, ceiling, packages, or other objects or surfaceswithin the space 101 to determine the 3D and 2D image data. The imagedata may be processed by the one or more processors and/or memories ofthe LMU 202 (or, in some embodiments, one or more remote processorsand/or memories of a server) to implement analysis, functions, such asgraphical or imaging analytics, as described by the one or more variousflowcharts, block diagrams, methods, functions, or various embodimentsherein. It should be noted that LMU 202 may capture 3D and/or 2D imagedata/datasets of a variety of loading facilities or other areas, suchthat additional loading facilities or areas (e.g., warehouses, etc.) inaddition to the predefined search spaces (e.g., space 101) arecontemplated herein.

In some embodiments, for example, LMU 202 may process the 3D and 2Dimage data/datasets, as scanned or sensed from the 3D camera andphoto-realistic camera, for use by other devices (e.g., server 301, asfurther described herein). For example, the one or more processorsand/or one or more memories of LMU 202 may capture and/or process theimage data or datasets scanned or sensed from space 101. The processingof the image data may generate post-scanning data that may includemetadata, simplified data, normalized data, result data, status data, oralert data as determined from the original scanned or sensed image data.In some embodiments, the image data and/or the post-scanning data may besent to a client device/client application, such as a container featureassessment app that may be, for example, installed and executing on aclient device, for viewing, manipulation, or otherwise interaction. Inother embodiments, the image data and/or the post-scanning data may besent to a server (e.g., server 301 as further described herein) forstorage or for further manipulation. For example, the image data and/orthe post-scanning data may be sent to a server, such as server 301. Insuch embodiments, the server or servers may generate post-scanning datathat may include metadata, simplified data, normalized data, resultdata, status data, or alert data as determined from the original scannedor sensed image data provided by LMU 202. As described herein, theserver or other centralized processing unit and/or storage may storesuch data, and may also send the image data and/or the post-scanningdata to a dashboard app, or other app, implemented on a client device,such as the container feature assessment app implemented on a clientdevice.

LMU 202 may include a mounting bracket 252 for orienting or otherwisepositioning the LMU 202 within a loading facility associated with space101 as described herein. The LMU 202 may further include one or moreprocessors and one or more memories for processing image data asdescribed herein. For example, the LMU 202 may include flash memory usedfor determining, storing, or otherwise processing the imagingdata/datasets and/or post-scanning data. In addition, LMU 202 mayfurther include a network interface to enable communication with otherdevices (such as server 301 of FIG. 3 as described herein). The networkinterface of LMU 202 may include any suitable type of communicationinterface(s) (e.g., wired and/or wireless interfaces) configured tooperate in accordance with any suitable protocol(s), e.g., Ethernet forwired communications and/or IEEE 802.11 for wireless communications.

FIG. 3 is a block diagram representative of an embodiment of a serverassociated with the loading facility of FIG. 1 and the LMU 202 of FIG.2. In some embodiments, server 301 may be located in the same facilityas loading facility of FIG. 1. In other embodiments, server 301 may belocated at a remote location, such as on a cloud-platform or otherremote location. In either embodiment, server 301 may be communicativelycoupled to a 3D camera (e.g., LMU 202).

Server 301 is configured to execute computer instructions to performoperations associated with the systems and methods as described herein,for example, implement the example operations represented by the blockdiagrams or flowcharts of the drawings accompanying this description.The server 301 may implement enterprise service software that mayinclude, for example, RESTful (representational state transfer) APIservices, message queuing service, and event services that may beprovided by various platforms or specifications, such as the J2EEspecification implemented by any one of the Oracle WebLogic Serverplatform, the JBoss platform, or the IBM Web Sphere platform, etc. Othertechnologies or platforms, such as Ruby on Rails, Microsoft .NET, orsimilar may also be used. As described below, the server 301 may bespecifically configured for performing operations represented by theblock diagrams or flowcharts of the drawings described herein.

The example server 301 of FIG. 3 includes a processor 302, such as, forexample, one or more microprocessors, controllers, and/or any suitabletype of processor. The example server 301 of FIG. 3 further includesmemory (e.g., volatile memory or non-volatile memory) 304 accessible bythe processor 302, for example, via a memory controller (not shown). Theexample processor 302 interacts with the memory 304 to obtain, forexample, machine-readable instructions stored in the memory 304corresponding to, for example, the operations represented by theflowcharts of this disclosure. Additionally or alternatively,machine-readable instructions corresponding to the example operations ofthe block diagrams or flowcharts may be stored on one or more removablemedia (e.g., a compact disc, a digital versatile disc, removable flashmemory, etc.), or over a remote connection, such as the Internet or acloud-based connection, that may be coupled to the server 301 to provideaccess to the machine-readable instructions stored thereon.

The example server 301 of FIG. 3 may further include a network interface306 to enable communication with other machines via, for example, one ormore computer networks, such as a local area network (LAN) or a widearea network (WAN), e.g., the Internet. The example network interface306 may include any suitable type of communication interface(s) (e.g.,wired and/or wireless interfaces) configured to operate in accordancewith any suitable protocol(s), e.g., Ethernet for wired communicationsand/or IEEE 802.11 for wireless communications.

The example server 301 of FIG. 3 includes input/output (I/O) interfaces308 to enable receipt of user input and communication of output data tothe user, which may include, for example, any number of keyboards, mice,USB drives, optical drives, screens, touchscreens, etc.

FIG. 4 is a flow chart for a 3D point cloud data process algorithm 400for assessing an orientation with respect to a container, in accordancewith example embodiments herein. Algorithm 400 describes various methodsfor automatic camera installation guidance, as described herein.Embodiments of the 3D point cloud data process algorithm 400 forautomatic camera installation guidance of FIG. 4 are discussed below incontext with FIGS. 5A-5B, 6A-6C, 7A-7B, 8A-8C, 9A-9B, 10, 11A-11B, and12A-12B.

Generally speaking, the 3D point cloud data process algorithm 400 ofFIG. 4 comprises three overarching stages. First, a 3D image of thecontainer is acquired by the 3D camera and converted to a 2D image forfurther processing. The 3D image consists of a point cloud of data whichis pre-processed consistent with the embodiments described herein. Forexample, the 3D point cloud data may be processed via methods such asdown sampling and outlier removal to identify the relevant data in the3D point cloud. The relevant 3D point cloud data may then be convertedto a 2D image, filled, and smoothed.

Next, the 2D image is matched to a template. In embodiments, thetemplate may be predetermined, and may be stored in a database as partof a set of predetermined templates. For example, each template of theset of predetermined templates may represent a specific container type.Thus, one predetermined template may be selected as a match for the 2Dimage based on the container type included in the 2D image. Moreover,each template may include associated characteristics to facilitatefurther processing of the 2D image. For example, the matching templatemay contain predetermined dimensions of the container type featured inthe template, such that the subsequent processing of the 2D image mayincorporate those known dimensions.

Finally, the container feature assessment algorithm uses this templatematch to determine various features of the container. For example, thealgorithm, as executed on a container feature assessment application(app), may perform segmentation on the 2D image to extract 3D features.From these 3D features, the app may determine both exterior and interiorfeatures of the container based on corresponding metrics. Subsequently,the app may generate an orientation adjustment instruction in responseto assessing the exterior and interior features. The orientationadjustment instruction may indicate to an operator an adjustment to theorientation of the 3D camera for use during a shipping container loadingsession.

As mentioned herein, one benefit, inter alia, of the systems and methodsof the present disclosure is the efficient assessment of the 3D camera'sorientation. This benefit is especially discernable in largeinstallation operations, where dozens of LMUs are installed at once.Moreover, the 3D cameras may require further orientation adjustmentsduring shipping container loading sessions. In these instances, thesystems and methods of the present disclosure enable an operator toquickly and efficiently make the necessary adjustments to the 3Dcamera's orientation to optimize uptimes, resulting in increasedproductivity, customer satisfaction, and overall system performance.Namely, instead of engaging in the traditional manual evaluation of the3D camera's images to determine approximate orientation adjustmentsbased on human visual assessments, the systems and methods of thepresent disclosure enable the operator to promptly receive a highlyaccurate orientation adjustment instruction in real-time.

In embodiments, 3D point cloud data process algorithm 400 may execute onone or more processors of LMU 202. Additionally or alternatively, 3Dpoint cloud data process algorithm 400 may execute in one or moreprocessors of server 301. For example, one or more processors may belocated at a server (e.g., server 301) and may be communicativelycoupled to the 3D camera via a digital network. Still further, 3D pointcloud data process algorithm 400 may execute on both LMU 202 and server301 in a client-server format, with a first portion of 3D point clouddata process algorithm 400 operating on LMU 202 and a second portion of3D point cloud data process algorithm 400 operating on server 301.

The 3D point cloud data process algorithm 400 may be executed as part ofa container feature assessment app. The container feature assessment appmay be software implemented in a programming language such as Java, C#,Ruby, etc., and compiled to execute on the one or more processors of LMU202 and/or server 301. For example, in embodiments, the containerfeature assessment app may include a “while” loop executing to performone or more portions of algorithm 400 upon receipt of 3D image data from3D camera. In such embodiments, receipt of the 3D image data wouldresult in a “true” condition or state that would trigger the while loopto execute the one or more portions of algorithm 400. In still furtherembodiments, the container feature assessment app may include one ormore event listeners, such as a listener function programmed within thecontainer feature assessment app, where the listener function wouldreceive, as a parameter, the 3D image data from 3D camera when the 3Dcamera captured 3D image data. In this way, the 3D camera would “push”3D image data to the listener function which would execute algorithm 400using the 3D image data as described herein.

More specifically, the 3D point cloud data process algorithm 400 beginsat block 402, where, for example, an application (e.g., containerfeature assessment app) receives point cloud input. In practice, the 3Dpoint cloud data process algorithm 400 utilizes a 3D camera (e.g., 3Dcamera 254 of LMU 202) configured to capture 3D image data. In variousembodiments, the 3D image data is 3D point cloud data. In addition, the3D image data may be captured periodically, such as every 30 seconds,every minute, or every two minutes, although other various rates (e.g.,other frame rates) and timings are contemplated herein.

In reference to FIG. 5A, the 3D camera (e.g., of LMU 202) is generallyoriented in a direction to capture 3D image data of a shipping container(e.g., shipping container 102) located in a space (e.g., space 101). Theshipping container may have a particular shipping container type, suchas type “AMJ” as shown for shipping container 102 of FIG. 1, or anyother type as described herein or as otherwise designated as a ULD typeof container.

The 3D point cloud data process algorithm 400, as part of the containerfeature assessment app, executing on processor(s) of LMU 202 and/orserver 301, may be configured to receive the 3D image data anddetermine, based on the 3D image data, a container point cloudrepresentative of the shipping container 102. FIG. 5A depicts an exampleembodiment of 3D image data representative of shipping container 102 ascaptured by 3D image camera 254 of LMU 202. As shown by FIG. 5A, 3Dimage data includes point cloud data 502, where point cloud data may berendered in different colors to represent different depths or distanceswithin the point cloud. For example, in the embodiment of FIG. 5A greenrepresents data nearer to 3D camera 254 and blue represents data furtheraway from 3D camera 254.

At block 404, the container feature assessment app is configured topre-process the 3D image data. Pre-processing the data may includevarious actions, such as down sampling or removing outliers from the 3Dimage data. For example, the container feature assessment app mayexecute one or more instructions to remove outliers from the 3D imagedata by first evaluating the distances associated with each point of the3D image data. The container feature assessment app may then compareeach distance to a threshold distance to eliminate those points in the3D image data that exceed, fall below, or otherwise fail to satisfy thethreshold requirement for inclusion in the subsequent execution of thealgorithm 400.

At block 406, the container feature assessment app is configured toexecute on one or more processors to convert the 3D image data into 2Ddepth image data. In general, the container feature assessment app mayconvert the any portion of the 3D image data into 2D depth image data.For example, the container feature assessment app may evaluate the 3Dimage data during preprocessing and eliminate a portion of the 3D imagedata associated with background surrounding the container. Thus, and inreference to FIG. 5B, the container feature assessment app may convert aportion of the 3D image data into 2D depth image data 504.

In embodiments, the container feature assessment app may be configuredto convert the 3D image data into 2D depth image data by generating the2D depth image data as grayscale image data. For example, and aspreviously mentioned, the 3D image data (e.g., 3D image data 502) may berendered in different colors to represent different depths or distanceswithin the point cloud. Thus, when the container feature assessment appconverts the 3D image data into the 2D depth image data 504, the app mayremove the rendered colors to generate a grayscale image.

At block 408, the container feature assessment app is configured toexecute on one or more processors to compare the 2D depth image data toone or more template image data. The one or more template image data maycorrespond to a respective shipping container type. For example, the oneor more template image data may correspond to shipping container typesincluding a ULD type being one of: an AMJ type, an AAD type, an AKEtype, an AYY type, a SAA type, an APE type, and/or an AQF type or anycombination thereof. Hence, in embodiments, the one or more templateimage data may correspond to one or more different ULD types.

In embodiments, the container feature assessment app may be configuredto generate the one or more template image data. Moreover, the containerfeature assessment app may generate the one or more template image databefore or during the execution of the algorithm 400. In general, the appmay receive 3D image point cloud data for a template shipping container.With reference to FIG. 6A, the app may then convert the 3D image pointcloud data to a depth image data 602. As shown in FIG. 6A, the depthimage data 602 may contain one or more holes 604. It is to beappreciated that the one or more holes 604 may each contain a pluralityof holes. The one or more holes 604 may represent a lack of data due to,for example, reflections from the metal surface of the ULD. In anyevent, the one or more holes 604 present an issue because the resultingtemplate may include portions of the depth image data 602 including theone or more holes 604. Thus, the one or more holes 604 may need to beeliminated from the depth image data 602 by including estimated data inplace of the one or more holes 604.

Thus, and as illustrated in FIG. 6B, the container feature assessmentapp may be configured to execute on one or more processors to completethe depth image data (e.g., depth image data 602). The completed depthimage 606 may represent the depth image data after the container featureassessment app has completed the data. For example, the containerfeature assessment app may utilize a penalized least squares method tofill in the missing values in the depth image data to create thecompleted depth image 606. However, it should be understood that anysuitable method may be used to fill in the missing data values, asrepresented by the one or more holes (e.g., one or more holes 604) ofthe depth image data.

The completed depth image 606, similar to the depth image data (e.g.,depth image data 602) may be rendered in different colors to representdifferent depths or distances within the image 606. However, such acolored representation is non-normal and therefore may causeinconsistent outcomes if used as a template for comparison duringreal-time image analysis. Thus, following the container featureassessment app completing the depth image data, the completed depthimage 606 may be normalized.

Correspondingly, the container feature assessment app may be furtherconfigured to execute on one or more processors to normalize thecompleted depth image 606. As illustrated in FIG. 6C, the containerfeature assessment app may convert the completed image (e.g., completeddepth image 606) into a grayscale 2D image 608. At this point, thegrayscale 2D image 608 may include a portion sufficient to create atemplate image. Hence, the container feature assessment app may proceedto extract a template portion from the grayscale 2D image 608.

As illustrated in FIG. 7, the container feature assessment app mayperform a template extraction 700. The template extraction 700 includesa grayscale 2D image 702 and template image data 704. The grayscale 2Dimage 702 may further include a template portion 706. In practice, anoperator of the LMU may determine and indicate the template portion 706,such that the container feature assessment app may store the selectedtemplate portion 706 as a standalone image (e.g., the template imagedata 704) for use in subsequent container feature assessments. However,in embodiments, the container feature assessment app may automaticallyidentify the template portion 706 in the grayscale 2D image 702.

To illustrate, and regardless of how the template portion 706 isidentified, the container feature assessment app may be configured toaccept the template image data 704 only if the data 704 includes aportion of the top, back-wall, and/or ground plane of the container(e.g., ULD). Moreover, the container feature assessment app may beconfigured to accept template image data 704 only if the data 704 iscentered on the portions of the top, back-wall, and/or ground planes inthe horizontal dimension. In any event, the template image data 704 maybe stored in memory until the container feature assessment appdetermines a container captured in a real-time 3D image matches thecontainer type featured in the data 704.

Thus, the container feature assessment app may compare the templateimage data 704 to any 2D depth image data (e.g., grayscale 2D image 608)to determine a match. For example, the container feature assessment appmay analyze the 2D depth image data by searching for portions of the 2Ddepth image data that match the characteristics of the template imagedata by a predetermined threshold amount. To illustrate, the containerfeature assessment app may search through the 2D depth image data toidentify a portion of the 2D depth image data that includes at least 90%consistent characteristics with the template image data 704. If thecontainer feature assessment app determines such a match exists, thecontainer feature assessment app may verify that a container exists inthe 2D depth image data and that the container is of a type consistentwith the known container type of the template image data 704. Throughthis analysis, the container feature assessment app may identifycontainers within 2D depth image data with high fidelity and in variouspositions to yield a robust identification process.

In embodiments, the container feature assessment app may analyze the 2Ddepth image data to determine a portion of the data sufficient forcomparison to the various stored templates (e.g., template image data704). The container feature assessment app may analyze the 2D depthimage to determine any suitable characteristics, but the app mayspecifically determine whether a particular portion includes a portionof the top, back-wall, and/or ground plane of the container. Similarly,the app may determine whether the particular portion is centered overthe container in the horizontal dimension. Once, the app determines aportion of the 2D depth image suitable for comparison to a template, theapp then determines a suitable template.

At block 410, the container feature assessment app is configured toexecute on one or more processors to perform segmentation on the 2Ddepth image data to extract 3D point cloud features. More specifically,the container feature assessment app is configured to extract the 3Dpoint cloud features in response to identifying a match between the 2Ddepth image data and one or more template image data. Namely, the app isconfigured to segment a ground plane and back-wall of the container fromwithin the 2D depth image data. The app will utilize the position of thetemplate image data to identify the ground and back-wall planes.

For example, and as illustrated in FIG. 8A, the 2D image data 800 may bean image of a container during an LMU installation session. Thecontainer feature assessment app may apply the template image data 704to the 2D image data 800 and determine a match between the templateimage data 704 and a portion of the 2D image data 800. Correspondingly,the container feature assessment app may determine positions of theback-wall 802 and ground plane 804 based on the position identified asmatching the template image data 704.

At block 412, the container feature assessment app may be configured toextract 3D point cloud features from the segmented back-wall 802 andground plane 804 through a regression algorithm. In embodiments, thecontainer feature assessment app may be configured to extract the 3Dpoint cloud features by feeding the back-wall 802 and ground plane 804into a plane regression pipeline, such that the plane regressionpipeline serves as the regression algorithm. The plane regressionpipeline may analyze the segmented back-wall 802 and ground plane 804portions and determine a set of 3D point cloud data corresponding toeach one.

For example, and as illustrated in FIG. 8B, the plane regressionpipeline may analyze the back-wall 802 segment to determine a back-wall3D point cloud 806. The back-wall 3D point cloud 806 may be a 3Drepresentation of the back-wall of the container, as extracted from 2Dimage data (e.g., 2D image data 800). Thus, the back-wall 3D point cloud806 may include characteristics of the physical back-wall of theshipping container (e.g., curvature, height, width, depth, etc.).

Similarly, and as illustrated in FIG. 8C, the plane regression pipelinemay analyze the ground plane 804 segment to determine a ground plane 3Dpoint cloud 808. The ground plane 3D point cloud 808 may be a 3Drepresentation of the ground plane of the container, as extracted from2D image data (e.g., 2D image data 800). Thus, the ground plane 3D pointcloud 808 may include characteristics of the physical ground plane ofthe shipping container (e.g., curvature, length, width, depth, etc.).

Moreover, the container feature assessment app may be further configuredto estimate the orientation of the camera during the regressionalgorithm. For example, the app may perform the regression algorithm(e.g., plane regression pipeline) and determine that the resulting 3Dpoint clouds (e.g., back-wall 3D point cloud 806 and ground plane 3Dpoint cloud 808) are not oriented with respect to the camera such thatthe remainder of the algorithm 400 may be successfully completed. As anexample, if the app determines that the ground plane and back-wall areaskew with respect to the orientation of the camera, the app may furtherdetermine that analyzing the front panel of the container will besignificantly complicated (and/or erroneously analyzed) due to themisalignment.

Thus, at block 414, the container feature assessment app may beconfigured to execute on one or more processors to rotate the 3D pointclouds (e.g., back-wall 3D point cloud 806 and ground plane 3D pointcloud 808). For example, the container feature assessment app may rotatethe 3D point clouds such that, after the rotation, the 3D point cloudsare more directly aligned with the line-of-sight (LOS) of the camera,oriented at an angle that is optimal for subsequent analysis, and/or anyother suitable rotation or combination thereof. In this manner, and asmentioned herein, rotating the 3D point clouds may serve to make thesubsequent computations more straightforward.

At block 416, the container feature assessment app is configured toexecute on one or more processors to localize a back-wall and a frontpanel of the container (e.g., shipping container 102). Generallyspeaking, the app may perform routines consistent with the algorithm 400to accurately determine the position of the back-wall and front panel ofthe container in 3D space. These routines may include analysis of therotated 3D point clouds, as described in FIG. 8C. In reference to FIG.9A, the container feature assessment app may perform this analysis in amanner that can be visualized similar to the schematic diagram 900. Thediagram features the LMU 202 having a field-of-view (FOV) composed of aback-wall search region 902 and a ground search region 904.

For example, the back-wall search region 902 may include a portion ofthe container similar to the segmented back-wall (e.g., segmentedback-wall 802). More specifically, the back-wall search region 902 mayinclude a portion of the container (e.g., shipping container 102)representing the back wall 906 of the container. Thus, the containerfeature assessment app may execute routines via the one or moreprocessors to prioritize the back-wall search region 902 to capture dataidentifiable with the back wall 906 of the container. In this manner,the container feature assessment app may simplify the analysis process,resulting in a more streamlined and efficient segmentation/regression ofthe container, and the algorithm 400 in general.

Similarly, the ground search region 904 may include a portion of thecontainer similar to the segmented ground plane (e.g., segmented groundplane 804). More specifically, the ground search region 904 may includea portion of the container (e.g., shipping container 102) representingthe bottom 908 of the container. Thus, the container feature assessmentapp may execute routines via the one or more processors to prioritizethe ground search region 904 to capture data identifiable with thebottom 908 of the container. In this manner, the container featureassessment app may simplify the analysis process, resulting in a morestreamlined and efficient segmentation/regression of the container, andthe algorithm 400 in general.

Moreover, the container feature assessment app may accurately localizethe front panel 910 of the container once the back-wall 906 and bottom908 have been localized. Each container type represented by acorresponding template image may include a back-wall height 912 and abottom distance 914. Thus, once the app localizes the back-wall 906 andbottom 908, the app may compare these localized features (906, 908) tothe known dimensions (912, 914).

As an example, the container feature assessment app may utilize the dataobtained via the back-wall search region 902 and the ground searchregion 904 to perform dimensional analysis of the container. Once theapp receives data representative of the back-wall 906 and bottom 908,the app may analyze the data to determine the back wall height 912 andthe bottom distance 914. In embodiments, both the back wall height 912and the bottom distance 914 may be known quantities based on thecontainer type (e.g., AMJ type, AAD type, AKE type, AYY type, SAA type,APE type, AQF type, etc.).

Particularly, the app may compare the bottom distance 914 to the depthcomponents of the localized features (906, 908) to determine where thefront panel 910 is likely located. Moreover, the app may compare theback-wall height 912 to the localized features (906, 908) to determinewhere the top of the front panel 910 is likely located. Correspondingly,the app may extract an estimated front panel based on the relationsbetween the localized features (906, 908) and the known dimensions (912,914).

At block 418, and in reference to FIG. 9B, the container featureassessment app may be configured to execute on one or more processors toperform a back-wall (e.g., back-wall 906) and front panel (e.g., frontpanel 910) plane regression. Similar to block 412, the plane regressionperformed by the container feature assessment app at block 418 may feedthe data representative of the back-wall and the front panel through aregression algorithm. In embodiments, the container feature assessmentapp may be configured to extract 3D point cloud features correspondingto the back-wall and/or the front panel by feeding the back-wall andfront panel data into a plane regression pipeline, such that the planeregression pipeline serves as the regression algorithm. The planeregression pipeline may analyze the data representative of the back-walland front panel to determine a set of 3D point cloud data correspondingto each one.

In embodiments, the container feature assessment app may be configuredto only perform a plane regression with respect to the front panel atblock 418. For example, the container feature assessment app may beconfigured to utilize the 3D point cloud data corresponding to theback-wall (e.g., back-wall 3D point cloud 806) generated at block 412 tosuffice for all or a portion of the algorithm 400. Regardless, the planeregression performed by the container feature assessment app withrespect to the front panel may yield a 3D point cloud representation ofthe front panel 910.

For example, the container feature assessment app may localize andperform a plane regression with respect to the front panel, yielding afront panel data 3D representation 920. The representation 920 mayinclude a front panel 3D point cloud 922. Moreover, the representation920 may include a set of bounding points. The set of bounding points mayinclude a set of left bounding points 924, a set of right boundingpoints 926, and a set of top bounding points 928. Each set of the set ofbounding points may correspond to a portion of the front panel. Forexample, the set of left bounding points 924 may correspond to the leftportion of the front panel. Each set of the set of bounding points maypartially and/or completely define the regions of interest in acompleteness analysis, in accordance with embodiments described herein.

At this point, the container feature assessment app has determined 3Dpoint cloud features corresponding to the back-wall of the container(e.g., back-wall 3D point cloud 806), the ground panel of the container(e.g., ground plane 3D point cloud 808), and the front panel of thecontainer (e.g., front panel 3D point cloud 922). Using these three 3Dpoint clouds, the container feature assessment app may be configured todetermine various features of the container using an exterior metric andan interior metric. The metrics may be any suitable evaluation metrics,and it is to be understood that the app may utilize any suitable numberand/or combination of evaluation metrics to make the exterior andinterior features determinations.

Namely, at block 420, the container feature assessment app may beconfigured to execute on one or more processors to perform acompleteness analysis with respect to the front panel. In embodiments,the completeness analysis may be the exterior metric the app uses todetermine exterior features of the container. For example, and inreference to FIG. 10, the container feature assessment app may renderthe 3D point cloud data representative of the front panel (e.g., frontpanel 3D point cloud 922) into a 2D format 1000. The 2D format mayinclude a left side portion 1002, a right side portion 1004, and a topedge portion 1006.

Generally speaking, each of the portions (1002, 1004, and 1006) mayinclude 2D data representative of the analogous portions of the frontpanel (e.g., the front panel 3D point cloud 922 data as bounded by theset of left bounding points 924, the set of right bounding points 926,and the set of top bounding points 928). By including the 3D point clouddata representative of the front panel into the respective portions, theapp may determine a completeness ratio of the data associated with eachportion (e.g., 1002, 1004, and 1006) by evaluating the amount ofreceived data in any particular portion (e.g., the number of pointsincluded in the target point cloud region (e.g., 924, 926, 928)) withrespect to the amount of received data in multiple portions.

For example, the 2D format 1000 may include a set of evaluation portions1008 a-1008 n, where n may be any suitable number of evaluationportions. Each portion of the set of evaluation portions 1008 a-1008 nmay include a set of data corresponding to the front panel of thecontainer. The left side portion 1002 may include, for example, 52individual evaluation portions 1008 a-1008 n. As shown in FIG. 10, thefront panel data (e.g., 2D data extracted from the front panel 3D pointcloud 922 data) included in the left side portion 1002 may represent theleft side of the front panel. The front panel data representative of theleft side of the front panel covers a portion of the left side portion1002, but several individual evaluation portions (e.g., 1008 n) may notinclude data representative of the left side of the front panel.Similarly, the front panel data included in the right side portion 1004may represent the right side of the front panel. The front panel datarepresentative of the right side of the front panel covers a portion ofthe right side portion 1004, but several individual evaluation portionsmay not include data representative of the right side of the frontpanel.

However, the amount of data available for a given container type may beinsufficient to fill any portion (1002, 1004, and 1006), so thecompleteness ratios may be calculated based on the relative number ofdata points acquired for any given side of the front panel. To evaluatethe completeness ratios, the container feature assessment app mayperform calculations consistent with the following:

$\begin{matrix}{{R_{l} = \frac{n_{l}}{n_{l} + n_{r}}},} & (1) \\{{R_{r} = \frac{n_{r}}{n_{l} + n_{r}}},} & (2) \\{{R_{u} = \frac{n_{u}}{n_{l} + n_{r} + n_{u}}},} & (3)\end{matrix}$

where n_(l), n_(r), and n_(u) may represent the number of front paneldata points included in the left side portion 1002, the right sideportion 1004, and the top edge portion 1006, respectively. Moreover,R_(l), R_(r), and R_(u) may represent the completeness ratios withrespect to the left side portion 1002, the right side portion 1004, andthe top edge portion 1006, respectively.

Thus, as mentioned and as indicated in each of the equations (1, 2, and3), the completeness ratios may depend on the relative number of pointsacquired for each side of the front panel. To illustrate, the containerfeature assessment app may be configured to evaluate whether enough ofthe front panel has been captured to make a valid assessment of theexterior features of the container. This evaluation may requiredetermining whether a particular portion of the container (e.g., theleft side) includes more data than another portion of the container(e.g., the right side).

For example, assume the left side portion 1002 includes a large volumeof data indicative of the left side of the container, and the right sideportion 1004 includes a significantly smaller volume of data indicativeof the right side of the container. In this example, the containerfeature assessment app may determine that the camera was not orientedcorrectly with respect to the container when acquiring the image data ofthe container. The app may further conclude that the orientation of thecamera should be adjusted such that the camera is oriented more to theright. In this manner, the camera may acquire a larger volume of datarepresentative of the right side of the container in subsequent imagecaptures, such that the app may adequately determine exterior featuresof the container.

Consequently, and in embodiments, the app may compare each completenessratio to a completeness ratio threshold. If the app determines that thecompleteness ratio for a particular side exceeds, is insufficient, orotherwise does not satisfy the completeness ratio threshold, the app maygenerate an orientation instruction based on that determination, asdescribed further herein. Regardless, once the completeness ratios havebeen computed, and the app determines that a satisfactory number of datapoints have been collected and evaluated for the front panel, the appmay then determine the interior features of the container.

Determining the interior features may begin at block 422, where thecontainer feature assessment app may be configured to execute on one ormore processors to estimate the height of the front panel and back-wallof the container. As illustrated in FIG. 11A, the app may perform thisestimation in a manner that can be visualized similar to the schematicdiagram 1100. The diagram 1100 includes a visible area 1102, a frontheight 1104, and a back-wall height 1106. The visible area 1102 maycorrespond to the FOV of the camera (e.g., LMU 202). Moreover, asmentioned herein and in embodiments, the container feature assessmentapp may receive the back-wall height 1106 in connection with a matchingimage template (e.g., template image data 704) for the 2D datarepresentative of the container.

In embodiments, the container feature assessment app may be configuredto execute on one or more processors to determine the front height 1104and the back-wall height 1006. The app may determine these heights basedon any suitable metric, but in embodiments may determine the frontheight 1104 and back-wall height 1006 based on an analysis of pointsassociated with the front panel and back-wall, respectively.

For example, and in reference to FIG. 11B, the app may evaluate thefront height 1104 and back-wall height 1106 in a composite 3Drepresentation 1120. The app may further designate points within therepresentation 1120 to make the height estimations. The app may assign afront panel highest point 1122 indicative of the highest point in alateral dimension associated with the front panel of the container.Similarly, the app may assign a back-wall highest point 1124 indicativeof the highest point in a lateral dimension associated with theback-wall of the container. Using these points (1122, 1124), the app mayestimate the front height 1104 and back-wall height 1106 by evaluatingheight coordinates for each point (1122, 1124), comparing the points(1122, 1124) to dimensions associated with the container type, and/orany other suitable estimation technique or combination thereof.

In any event, and as indicated at block 424, once the app determinesestimated values for the front height 1104 and the back-wall height1106, the app may calculate an occlusion ratio for the acquired data.Generally, the occlusion ratio indicates an amount of the container thatis obscured from the FOV of the camera (e.g., LMU 202). The occlusionratio may be defined as:

$\begin{matrix}{{R_{o} = \frac{H_{b}}{H_{f}}},} & (4)\end{matrix}$

where H_(b) and H_(f) indicate the back-wall height 1106 and frontheight 1004, respectively; and R_(o) indicates the occlusion ratio. Morespecifically, the occlusion ratio may be inversely proportional to theamount of the container that is obscured from the FOV of the camera. Toillustrate, assume the back-wall height 1106 is 10 feet, and that thefront height 1104 is 1 foot. In this situation, the occlusion ratiowould be 10:1, or simply 10. Thus, the FOV of the camera would beobscured to a large portion of the container interior because the topedge of the front panel would be significantly shorter than the top edgeof the back-wall.

Consequently, to ensure that the camera may adequately image and analyzethe interior of each container, for example, during loading sessions,the app may compare the occlusion ratio to an occlusion ratio threshold.If the app determines that the occlusion ratio for a particularcontainer exceeds, is insufficient, or otherwise does not satisfy theocclusion ratio threshold, the app may generate an orientationinstruction based on that determination, as described further herein.

At block 426, the container feature assessment app may be configured toexecute on one or more processors to verify the visibility and occlusionstatuses of the container. For example, the app may verify that thecompleteness ratios and the occlusion ratio satisfy any associatedthreshold values, such that the app may begin creating a 3D bounding boxfor the container.

In reference to FIG. 12A, the app may begin determining a bounding boxby generating a 3D representation 1200 of the container data. Forexample, the representation 1200 may include the composite 3Drepresentation 1120 of the container 3D point cloud data, and a set ofplanes 1202 a-1202 f that the app may fit to the composite 3Drepresentation 1120.

In embodiments, the app may fit each plane of the set of planes 1202a-1202 f based on a fitting algorithm. The fitting algorithm may beginfitting the set of planes 1202 a-1202 f to the representation 1120 basedon assumptions, such as that the left plane 1202 e and the right plane1202 f are vertical with respect to the front plane 1202 c and theground plane 1202 b. Based on the acquired 3D point cloud data for thecontainer (e.g., back-wall 3D point cloud 806, ground plane 3D pointcloud 808, and front panel 3D point cloud 922), in conjunction with theassumptions made by the fitting algorithm, the app may fit the set ofplanes 1202 a-1202 f to the 3D point cloud data. The app may place eachplane of the set of planes 1202 a-1202 f as close to each correspondingside of the container until the respective plane touches one or morepoints in the respective point cloud. However, it should be understoodthat the app may fit each plane of the set of planes 1202 a-1202 f inany suitable fashion. Moreover, it is to be appreciated that the app mayfit any suitable number of planes to the 3D point cloud data.

Nevertheless, once the container feature assessment app fits the sixplanes comprising the set of planes 1202 a-1202 f to the 3D point clouddata, the app may further determine a bounding box associated with the3D point cloud data. For example, and as illustrated in FIG. 12B, thecontainer feature assessment app may generate a 3D representation 1220including the composite 3D representation 1120, and a bounding box 1222.The bounding box 1222 may be representative of portions of the sixplanes generated as part of the fitting process (e.g., set of planes1202 a-1202 f). In embodiments, the bounding box 1222 may include thefront panel highest point 1122 and the back-wall highest point 1124,and/or the app may determine the bounding box 1222 based, in part, onthe location and values of the points (1122, 1124).

Generally speaking, after the container feature assessment app evaluatesthe exterior and interior features of the container, the app maygenerate an orientation adjustment instruction. The orientationadjustment instruction may indicate to an operator to orient the 3Dcamera (e.g., LMU 202) from the first direction of the camera to asecond direction. The second direction of the camera may be differentfrom the first direction of the camera. Moreover, the second directionof the camera may be for use during a shipping container loadingsession. Thus, the second direction may place the camera in anorientation such that the camera may adequately image each subsequentcontainer placed in the FOV of the camera to perform satisfactorycontainer analytics.

In embodiments, the orientation adjustment instruction may be generatedin response to the container feature assessment app determining thebounding box. The orientation adjustment instruction may include atleast one of an orientation to right instruction, an orientation to leftinstruction, a lower/raise instruction, and a tilt up/down instruction.Thus, the app may generate an instruction indicating to an operator thatthe camera is tilted too far down, and should be tilted up to place thecamera in a more satisfactory orientation to perform containeranalytics.

In embodiments, the container feature assessment app may communicatewith an installation visualization app. The installation visualizationapp may execute on a client device implementing a graphical userinterface (GUI). The GUI may graphically indicate the orientationadjustment instruction on a digital display of the client device. Forexample, the GUI may display the orientation adjustment instruction asan arrow indicating the direction of adjustment intended by theorientation adjustment instruction. However, it should be understoodthat the graphical indication of the orientation adjustment instructionmay include any alphanumeric character, symbol, image, video, and/or anyother suitable indication or combination thereof.

Further in these embodiments, the installation visualization app may beconfigured to display the orientation adjustment instruction on a headsup display (HUD) communicatively coupled to the 3D camera via a digitalnetwork. The installation visualization app may also comprise aninstallation voice instruction app. The installation voice instructionapp may execute on a client device implementing a speaker for audiblycommunicating the orientation adjustment instruction to the operator ofthe client device. Thus, the container feature assessment app maycommunicate the orientation adjustment instruction to the installationvoice instruction app, which may then audibly communicate theorientation adjustment instruction to the operator.

In embodiments, the client device may implement the installationvisualization app to receive the image data and/or the post-scanningdata and display such data, e.g., in graphical or other format, to amanager or loader to facilitate the unloading or loading of packages(e.g., 104, 107, etc.), as described herein. In some embodiments, theinstallation visualization app may be implanted as part of ZebraTechnologies Corps.'s SmartPack™ container loading analytics (CLA)solution. The installation visualization app may be installed on clientdevices operating in loading and shipping facilities (e.g., a loadingfacility as depicted by FIG. 1). The installation visualization app maybe implemented via a web platform such as Java J2EE (e.g., Java ServerFaces) or Ruby on Rails. In such embodiments, the web platform maygenerate or update a user interface of the dashboard app via generationof a dynamic webpage (e.g., using HTML, CSS, JavaScript) or via aclient-facing mobile app (e.g., via Java for a Google Android based appor Objective-C/Swift for an Apple iOS based app), where the userinterface is displayed via the dashboard app on the client device.

In embodiments, the installation visualization app may receive the imagedata/datasets and/or the post-scanning data and display such data inreal-time. The client device may be a mobile device, such as a tablet,smartphone, laptop, or other such mobile computing device. The clientdevice may implement an operating system or platform for executing thedashboard (or other) apps or functionality, including, for example, anyof the Apple iOS platform, the Google Android platform, and/or theMicrosoft Windows platform. The client device may include one or moreprocessors and/or one or more memories implementing the dashboard app orfor providing other similar functionality. The client device may alsoinclude wired or wireless transceivers for receiving image data and/orpost-scanning data as described herein. Such wired or wirelesstransceivers may implement one or more communication protocol standardsincluding, for example, TCP/IP, WiFi (802.11b), Bluetooth, or any othersimilar communication protocols or standards.

Generally, as would be understood by one of skill in the art from thepresent disclosure, certain benefits accrue from the techniques andfeatures described herein. The 3D depth imaging systems and methodsdescribed herein provide a feature assessment technique to determine aninitial orientation configuration (e.g., generate any necessaryorientation adjustment instructions) for a further dynamicauto-orientation purpose during subsequent analyses, as necessary. Inaddition, the 3D depth imaging systems and methods described hereinallow for segmentation and regression fitting of a target container'sfront panel, back-wall, and ground panel based on the acquired 3D depthimage data.

The 3D depth imaging systems and methods described herein providecompleteness ratio and occlusion ratio determination techniques, as partof algorithm 400, that accurately estimates ULD visibility with respectto the LMU. In addition, the 3D depth imaging systems and methodsdescribed herein include a unique technique for fitting bounding planesto the top, bottom, left, right, front, and back edges of the containerbased on an outside-to-inside approach which is robust for varioustypes/shapes of containers. Further, the 3D depth imaging systems andmethods described herein provide a technique for automaticallygenerating orientation adjustment instructions of the camera, based onthe exterior and interior features analysis. These orientationadjustment determinations were traditionally performed manually, andthus were generally inaccurate and time-consuming. Consequently, the 3Ddepth imaging systems and methods described herein greatly reduce thetime required to accurately orient the imaging system in order toperform advanced container analytics.

In the foregoing specification, specific embodiments have beendescribed. However, one of ordinary skill in the art appreciates thatvarious modifications and changes can be made without departing from thescope of the invention as set forth in the claims below. Accordingly,the specification and figures are to be regarded in an illustrativerather than a restrictive sense, and all such modifications are intendedto be included within the scope of present teachings.

The benefits, advantages, solutions to problems, and any element(s) thatmay cause any benefit, advantage, or solution to occur or become morepronounced are not to be construed as a critical, required, or essentialfeatures or elements of any or all the claims. The invention is definedsolely by the appended claims including any amendments made during thependency of this application and all equivalents of those claims asissued.

Moreover, in this document, relational terms such as first and second,top and bottom, and the like may be used solely to distinguish oneentity or action from another entity or action without necessarilyrequiring or implying any actual such relationship or order between suchentities or actions. The terms “comprises,” “comprising,” “has”,“having,” “includes”, “including,” “contains”, “containing” or any othervariation thereof, are intended to cover a non-exclusive inclusion, suchthat a process, method, article, or apparatus that comprises, has,includes, contains a list of elements does not include only thoseelements but may include other elements not expressly listed or inherentto such process, method, article, or apparatus. An element proceeded by“comprises . . . a”, “has . . . a”, “includes . . . a”, “contains . . .a” does not, without more constraints, preclude the existence ofadditional identical elements in the process, method, article, orapparatus that comprises, has, includes, contains the element. The terms“a” and “an” are defined as one or more unless explicitly statedotherwise herein. The terms “substantially”, “essentially”,“approximately”, “about” or any other version thereof, are defined asbeing close to as understood by one of ordinary skill in the art, and inone non-limiting embodiment the term is defined to be within 10%, inanother embodiment within 5%, in another embodiment within 1% and inanother embodiment within 0.5%. The term “coupled” as used herein isdefined as connected, although not necessarily directly and notnecessarily mechanically. A device or structure that is “configured” ina certain way is configured in at least that way, but may also beconfigured in ways that are not listed.

It will be appreciated that some embodiments may be comprised of one ormore generic or specialized processors (or “processing devices”) such asmicroprocessors, digital signal processors, customized processors andfield programmable gate arrays (FPGAs) and unique stored programinstructions (including both software and firmware) that control the oneor more processors to implement, in conjunction with certainnon-processor circuits, some, most, or all of the functions of themethod and/or apparatus described herein. Alternatively, some or allfunctions could be implemented by a state machine that has no storedprogram instructions, or in one or more application specific integratedcircuits (ASICs), in which each function or some combinations of certainof the functions are implemented as custom logic. Of course, acombination of the two approaches could be used.

Moreover, an embodiment can be implemented as a computer-readablestorage medium having computer readable code stored thereon forprogramming a computer (e.g., comprising a processor) to perform amethod as described and claimed herein. Examples of suchcomputer-readable storage mediums include, but are not limited to, ahard disk, a CD-ROM, an optical storage device, a magnetic storagedevice, a ROM (Read Only Memory), a PROM (Programmable Read OnlyMemory), an EPROM (Erasable Programmable Read Only Memory), an EEPROM(Electrically Erasable Programmable Read Only Memory), and a Flashmemory. Further, it is expected that one of ordinary skill,notwithstanding possibly significant effort and many design choicesmotivated by, for example, available time, current technology, andeconomic considerations, when guided by the concepts and principlesdisclosed herein will be readily capable of generating such softwareinstructions and programs and ICs with minimal experimentation.

The Abstract of the Disclosure is provided to allow the reader toquickly ascertain the nature of the technical disclosure. It issubmitted with the understanding that it will not be used to interpretor limit the scope or meaning of the claims. In addition, in theforegoing Detailed Description, it can be seen that various features aregrouped together in various embodiments for the purpose of streamliningthe disclosure. This method of disclosure is not to be interpreted asreflecting an intention that the claimed embodiments require morefeatures than are expressly recited in each claim. Rather, as thefollowing claims reflect, inventive subject matter lies in less than allfeatures of a single disclosed embodiment. Thus, the following claimsare hereby incorporated into the Detailed Description, with each claimstanding on its own as a separately claimed subject matter.

What is claimed is:
 1. A non-transitory computer-readable medium storingcomputer-readable instructions that, when executed by a one or moreprocessors, causes a mobile device to execute a container featureassessment application (app), the mobile device configured to receive 3Dimage data captured by a 3D-depth camera, the 3D-depth camera orientedin a first direction to capture the 3D image data of a shippingcontainer located in a space, the shipping container having a shippingcontainer type, the container feature assessment app being configuredto: execute on the one or more processors and to receive the 3D imagedata, the container feature assessment app configured to determine,based on the 3D image data, a container point cloud representative ofthe shipping container, wherein the container feature assessment app isfurther configured to execute on the one or more processors to: (a)convert the 3D image data into 2D depth image data; (b) compare the 2Ddepth image data to one or more template image data, each of the one ormore template image data corresponding to a respective shippingcontainer type; (c) in response to a match between the 2D depth imagedata and the one or more template image data, perform segmentation toextract 3D point cloud features; (d) from the 3D point cloud features,determine exterior features of the shipping container and assess theexterior features using an exterior features metric; (e) from the 3Dpoint cloud features, determine interior features of the shippingcontainer and assess the interior features using an interior featuresmetric; and (f) in response to assessing the exterior features andassessing the interior features, generate an orientation adjustmentinstruction for indicating to an operator to orient the 3D-depth camerain a second direction for use during a shipping container loadingsession, wherein the second direction is different than the firstdirection.
 2. The non-transitory computer-readable medium of claim 1,wherein the container feature assessment app is further configured toconvert the 3D image data into 2D depth image data by generating the 2Ddepth image data as grayscale image data, and wherein the containerfeature assessment app is further configured to compare the 2D depthimage data to the one or more template image data in grayscale toidentify a template matching portion.
 3. The non-transitorycomputer-readable medium of claim 2, wherein the container featureassessment app is further configured to execute on the one or moreprocessors to: receive 3D image point cloud data for a template shippingcontainer, convert the 3D image point cloud data to a depth image data,apply a penalized least squares process to introduce missing data intothe depth image data and generate a template depth image, and normalizethe template depth image to a grayscale to generate the template imagedata.
 4. The non-transitory computer-readable medium of claim 2, whereinthe container feature assessment app is further configured to execute onthe one or more processors to: segment the 3D image data into a shippingcontainer ground plane and a shipping container back wall based on thetemplate matching portion; and feed the shipping container ground planeand the shipping container back wall to a plane regression pipeline anddetermine and segment a shipping container front plane, wherein the 3Dpoint cloud features comprise the shipping container ground plane, theshipping container back wall, and the shipping container front plane. 5.The non-transitory computer-readable medium of claim 4, wherein thecontainer feature assessment app is further configured to assess theexterior features using the exterior features metric by: dividing theshipping container front plane into a left side portion, a right sideportion, and a top edge portion, determining completeness ratios foreach of the left side portion, the right side portion, and the top edgeportion, and determining, from the completeness ratios, a completenessof the shipping container front plane as the exterior features metric.6. The non-transitory computer-readable medium of claim 5, wherein thecontainer feature assessment app is further configured to assess theinterior features using the interior features metric by: determining aheight of the shipping container front plane, determining a height ofthe shipping container back wall, and determining an occlusion ratio asthe interior features metric.
 7. The non-transitory computer-readablemedium of claim 6, wherein the container feature assessment app isfurther configured to execute on the one or more processors to:determine six planes of the shipping container; determine a bounding boxof the shipping container based on the six planes; and generate theorientation adjustment instruction in response to determining thebounding box, the orientation adjustment instruction comprising at leastone of an orientation to right instruction, an orientation to leftinstruction, a lower/raise instruction, and a tilt up/down instruction.8. The non-transitory computer-readable medium of claim 1, wherein the3D-depth camera and the one or more processors are housed in a loadmonitoring unit (LMU).
 9. The non-transitory computer-readable medium ofclaim 1, wherein the one or more template image data corresponds to oneor more different universal loading device (ULD) types.
 10. Thenon-transitory computer-readable medium of claim 1, further comprisingan installation visualization app, the installation visualization appexecuting on a client device implementing a graphical user interface(GUI), the GUI graphically indicating the orientation adjustmentinstruction on a digital display of the client device.
 11. Thenon-transitory computer-readable medium of claim 10, wherein theinstallation visualization app is configured to display the orientationadjustment instruction on a heads up display (HUD) communicativelycoupled to the 3D-depth camera via a digital network.
 12. Thenon-transitory computer-readable medium of claim 1, further comprisingan installation voice instruction app, the installation voiceinstruction app executing on a client device implementing a speaker foraudibly communicating the orientation adjustment instruction to theoperator of the client device.